Database Queries, Data Mining, and OLAP

2011 ◽  
pp. 339-343
Author(s):  
Lutz Hamel
Keyword(s):  
Data Mining ◽  
Relational Database ◽  
Query Language ◽  
Data Retrieval ◽  
Database Systems ◽  
Database Queries ◽  
Analytical Processing ◽  
Relational Database Systems ◽  
Complementary Value ◽  
Do So

Modern, commercially available relational database systems now routinely include a cadre of data retrieval and analysis tools. Here we shed some light on the interrelationships between the most common tools and components included in today’s database systems: query language engines, data mining components, and online analytical processing (OLAP) tools. We do so by pair-wise juxtaposition, which will underscore their differences and highlight their complementary value.

Database Queries, Data Mining, and OLAP

2011 ◽  
pp. 598-603
Author(s):  
Lutz Hamel
Keyword(s):  
Data Mining ◽  
Relational Database ◽  
Query Language ◽  
Data Retrieval ◽  
Database Systems ◽  
Database Queries ◽  
On Line ◽  
Analytical Processing ◽  
Relational Database Systems ◽  
Complementary Value

Modern, commercially available relational database systems now routinely include a cadre of data retrieval and analysis tools. Here we shed some light on the interrelationships between the most common tools and components included in today’s database systems: query language engines, data mining components, and on-line analytical processing (OLAP) tools. We do so by pair-wise juxtaposition which will underscore their differences and highlight their complementary value.

scholarly journals Analysis of the possibilities of optimizing SQL queries

2021 ◽  
Vol 19 ◽  
pp. 151-158
Author(s):  
Piotr Rymarski ◽  
Grzegorz Kozieł
Keyword(s):  
Relational Database ◽  
Web Applications ◽  
Query Language ◽  
Database Systems ◽  
Sql Server ◽  
Management Systems ◽  
Relational Database Systems ◽  
The Impact ◽  
Relational Database Management ◽  
Test Scenarios

Most of today's web applications run on relational database systems. Communication with them is possible through statements written in Structured Query Language (SQL). This paper presents the most popular relational database management systems and describes common ways to optimize SQL queries. Using the research environment based on fragment of the imdb.com database, implementing OracleDb, MySQL, Microsoft SQL Server and PostgreSQL engines, a number of test scenarios were performed. The aim was to check the performance changes of SQL queries resulting from syntax modication while maintaining the result, the impact of database organization, indexing and advanced mechanisms aimed at increasing the eciency of operations performed, delivered in the systems used. The tests were carried out using a proprietary application written in Java using the Hibernate framework.

scholarly journals Data Mining

2008 ◽  
Vol 12 (1) ◽  
pp. 17-24
Author(s):  
Ihssan Alkadi
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Relational Database ◽  
Database Systems ◽  
Knowledge Bases ◽  
Query Languages ◽  
Business Strategies ◽  
Modeling Tools ◽  
Relational Database Systems ◽  
Mining Tools

Recently data mining has become more popular in the information industry. It is due to the availability of huge amounts of data. Industry needs turning such data into useful information and knowledge. This information and knowledge can be used in many applications ranging from business management, production control, and market analysis, to engineering design and science exploration. Database and information technology have been evolving systematically from primitive file processing systems to sophisticated and powerful databases systems. The research and development in database systems has led to the development of relational database systems, data modeling tools, and indexing and data organization techniques. In relational database systems data are stored in relational tables. In addition, users can get convenient and flexible access to data through query languages, optimized query processing, user interfaces and transaction management and optimized methods for On-Line Transaction Processing (OLTP). The abundant data, which needs powerful data analysis tools, has been described as a data rich but information poor situation. The fast-growing, tremendous amount of data, collected and stored in large and numerous databases. Humans can not analyze these large amounts of data. So we need powerful tools to analyze this large amount of data. As a result, data collected in large databases become data tombs. These are data archives that are seldom visited. So, important decisions are often not made based on the information-rich data stored in databases rather based on a decision maker's intuition. This is because the decision maker does not have the tools to extract the valuable knowledge embedded in the vast amounts of data. Data mining tools which perform data analysis may uncover important data patterns, contributing greatly to business strategies, knowledge bases, and scientific and medical research. So data mining tools will turn data tombs into golden nuggets of knowledge.

Vertical Database Design for Scalable Data Mining

2008 ◽  
pp. 3694-3699
Author(s):  
William Perrizo ◽  
Qiang Ding ◽  
Masum Serazi ◽  
Taufik Abidin ◽  
Baoying Wang
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Relational Database ◽  
Large Data ◽  
Database Design ◽  
Database Systems ◽  
Good Sense ◽  
Data Repositories ◽  
Relational Database Systems ◽  
Scalable Data Mining

For several decades and especially with the preeminence of relational database systems, data is almost always formed into horizontal record structures and then processed vertically (vertical scans of files of horizontal records). This makes good sense when the requested result is a set of horizontal records. In knowledge discovery and data mining, however, researchers are typically interested in collective properties or predictions that can be expressed very briefly. Therefore, the approaches for scan-based processing of horizontal records are known to be inadequate for data mining in very large data repositories (Han & Kamber, 2001; Han, Pei, & Yin, 2000; Shafer, Agrawal, & Mehta, 1996).

Access path query language for relational database systems

1983 ◽  
Vol 13 (8) ◽  
pp. 661-670
Author(s):  
L. M. Patnaik ◽  
Phule Shailendra ◽  
K. Venkateswara Rao
Keyword(s):  
Relational Database ◽  
Query Language ◽  
Database Systems ◽  
Access Path ◽  
Relational Database Systems

Data mining algorithms as a service in the cloud exploiting relational database systems

2013 ◽  
Author(s):  
Carlos Ordonez ◽  
Javier García-García ◽  
Carlos Garcia-Alvarado ◽  
Wellington Cabrera ◽  
Veerabhadran Baladandayuthapani ◽  
...  
Keyword(s):  
Data Mining ◽  
Relational Database ◽  
Database Systems ◽  
Data Mining Algorithms ◽  
Relational Database Systems ◽  
Mining Algorithms

Vertical Database Design for Scalable Data Mining

2005 ◽  
pp. 736-739
Author(s):  
William Perrizo ◽  
Qiang Ding ◽  
Masum Serazi ◽  
Taufik Abidin ◽  
Baoying Wang
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Relational Database ◽  
Large Data ◽  
Database Design ◽  
Database Systems ◽  
Good Sense ◽  
Data Repositories ◽  
Relational Database Systems ◽  
Scalable Data Mining

For several decades and especially with the preeminence of relational database systems, data is almost always formed into horizontal record structures and then processed vertically (vertical scans of files of horizontal records). This makes good sense when the requested result is a set of horizontal records. In knowledge discovery and data mining, however, researchers are typically interested in collective properties or predictions that can be expressed very briefly. Therefore, the approaches for scan-based processing of horizontal records are known to be inadequate for data mining in very large data repositories (Han & Kamber, 2001; Han, Pei, & Yin, 2000; Shafer, Agrawal, & Mehta, 1996).

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